SKILLEMALL.ai

BD smyx-fish-abnormal-swimming-detection-analysis

Through fixed cameras on aquariums, the system analyzes fish swimming videos and computes the angle between the fish body axis and the horizontal plane (normal fish bodies stay nearly horizontal). | 通过鱼缸固定摄像头,分析鱼类的游动视频,检测鱼体轴线与水平面的夹角(正常鱼体基本保持水平),当鱼体倾斜角度超过阈值(默认 > 30°)或出现倒立(头部向下 > 45°)、旋转(绕自身纵轴连续翻转)等异常游姿时,标记为异常,并记录异常时长占观察总时长的比例。该技能有助于早期发现鱼鳔失调、神经系统疾病或水质中毒等健康问题,提醒养鱼爱好者及时干预。

ClawHub Agent Skills author: smyx-sunjinhui v1.0.9 MIT-0 30 files body ≈ 2 067 tokens Open the sourceclawhub.ai analyzed 2 d ago

Through fixed cameras on aquariums, the system analyzes fish swimming videos and computes the angle between the fish body axis and the horizontal plane…

As a process D 35/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

AnalyzerMedia and videoSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
D
35/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 30. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 35/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 25Steps. 1 steps
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2067 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • -265 emoji in the instructions: noise for the model
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 371: enough signal without eating the budget
  • +4Structure: 19 headings
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
The skill has a coherent aquarium-video analysis purpose, but it silently creates or reuses user identity state and the packaged default configuration can send videos and credentials over plaintext development HTTP endpoints.
LLM: suspicious (high) · 8 Sept 2026